The present invention relates to learning for weak classifiers.
Object detection refers generally to a process to locate objects of interest (e.g., faces and pedestrians) in images and videos. Given a testing images, an object detector searches all possible positions for existence of targets.
Object detection is essentially a classification problem. A technique called boosted cascade has been quite effective in this task due to its high accuracy and efficiency. This classification model combines a series of less accurate yet very efficient “weak” classifiers to constitute a highly accurate “strong” one to distinguish object patterns from background ones.
Conventional systems have selected a suitable threshold to divide a 1-D Haar-like feature into two sub-regions, which can only roughly distinguish samples of different categories. Other conventional approaches have used a much finer partition for each 1-D feature to mitigate this problem; however, this partition is defined beforehand to produce sub-regions of equal width, which fails in adapting to the distribution of training samples and its variation during boosting procedure. Yet other systems have achieved joint partition of multiple-dimension feature space by binary partition of each feature space, these partitions are learned sequentially, purely supervised, and no features are shared between weak classifiers.
Compared to weak parametric models, weak non-parametric models such as decision stumps or trees have been broadly adopted in a number of object detection systems due to their simplicity and flexibility. In certain systems, a weak classifier is simply a decision stump upon a 1-dimensional Haar-like rectangular feature. This has been extended to multi-stump weak classifiers where the system partitions the 1-dimensional Haar-like feature into multiple sub-regions of equal width instead of a binary partition given by the decision stump. In other known systems, joint Haar-like feature based weak classifiers concatenate binary decision stumps over several 1-dimensional Haar-like features to achieve similar multiple sub-region partition; both compute optimal outputs for each sub-regions respectively.